Assessing Habitat Suitability for Native and Alien Freshwater Mussels in the River Waal (the Netherlands), Using Hydroacoustics and Species Sensitivity Distributions

Longitudinal training dams (LTDs) in the river Waal are novel river training structures that protect the littoral zone from the adverse effects of navigation providing new habitats for riverine macroinvertebrates. In order to inform river management and to better understand their ecological value for native and alien mussel species, it is important to assess the habitat suitability of the protected LTD shore channels. We applied spatial hydroacoustics surveys consisting of side-scan sonar (SSS) and acoustic Doppler current profiler (ADCP) of the substrate type, water depth and flow velocity in three shore channels in combination with species sensitivity distributions (SSDs) to predict habitat suitability for native and alien mussel species. SSDs allowed for the prediction of habitat suitability as a potentially occurring fraction (POF) of a species pool. High substrate type, water depth, and near-bottom flow velocity POFs were found for ≥ 70%, 100%, and 4–51% of the total shore channel area, respectively, suggesting that shore channels provide suitable habitat for both native and alien mussel species. To enhance the shore channels as habitat for native mussel species, we recommend increasing shallow areas dominated by fine (silt/clay) and sand substrate types with low near-bottom flow velocities (near 0 m/s). In contrast, the total area of hard substrate (e.g., boulders) in the shore channels should be reduced as it strongly favored invasive alien mussel species in our study. Future research should include additional abiotic parameters to enhance the habitat suitability predictions and compare the results for different riverine habitats.

Assessing the Impacts of Chloride and Sulfate Ions on Macroinvertebrate Communities in Ohio Streams

Salinization of freshwaters is a growing concern, especially in urban catchments. Existing aquatic life criteria for chloride (230 mg/L; a US standard) or total dissolved solids (1500 mg/L; specific to Ohio) do not protect sensitive species, and standards for sulfate have yet to be promulgated on the national level. To help identify water quality thresholds for protection and restoration, species sensitivity distributions were compiled for chloride and sulfate based on field observations of macroinvertebrate communities co-located with water quality samples obtained from rivers and streams throughout Ohio. Additionally, attainment of biological benchmarks for macroinvertebrate communities found in headwater streams were modeled against chloride and sulfate using Bayesian logistic regression. The hazard concentration based on statewide data for chloride was 52 mg/L. The hazard concentration for sulfate based on data from the Western Allegheny Plateau ecoregion was 152 mg/L. The median effect levels from logistic regression for chloride and sulfate varied by ecoregion. Mayfly taxa were disproportionately represented in taxa comprising the lower 5th percentile of the species sensitivity distributions for chloride. However, logistic regression models of individual taxa response (as presence/absence) revealed that some taxa considered sensitive to pollution in general were highly tolerant of chloride. For 166 taxa showing directional response to chloride, 91 decreased and 75 increased. For the 97 individual taxa showing directional responses to sulfate, 81 decreased. Of the 16 taxa showing an increase, 6 are considered tolerant of pollution, 9 facultative and 1 moderately intolerant, the latter being taxa in the dipteran family Tipulidae. The hazard concentrations are useful as protective thresholds for existing high-quality waters. The logistic regression model of attainment can be used to inform management goals conditional on site-specific information.

Estimating species sensitivity distributions on the basis of readily obtainable descriptors and toxicity data for three species of algae, crustaceans, and fish

Estimation of species sensitivity distributions (SSDs) is a crucial approach to predicting ecological risks and water quality benchmarks, but the amount of data required to implement this approach is a serious constraint on the application of SSDs to chemicals for which there are few or no toxicity data. The development of statistical models to directly estimate the mean and standard deviation (SD) of the logarithms of log-normally distributed SSDs has recently been proposed to overcome this problem. To predict these two parameters, we developed multiple linear regression models that included, in addition to readily obtainable descriptors, the mean and SD of the logarithms of the concentrations that are acutely toxic to one algal, one crustacean, and one fish species, as predictors. We hypothesized that use of the three species’ mean and SD would improve the accuracy of the predicted means and SDs of the logarithms of the SSDs. We derived SSDs for 60 chemicals based on quality-assured acute toxicity data. Forty-five of the chemicals were used for model fitting, and 15 for external validation. Our results supported previous findings that models developed on the basis of only descriptors such as log KOW had limited ability to predict the mean and SD of SSD (e.g., r2 = 0.62 and 0.49, respectively). Inclusion of the three species’ mean and SD, in addition to the descriptors, in the models markedly improved the predictions of the means and SDs of SSDs (e.g., r2 = 0.96 and 0.75, respectively). We conclude that use of the three species’ mean and SD is promising for more accurately estimating an SSD and thus the hazardous concentration for 5% of species in cases where limited ecotoxicity data are available.